Overview Figures

Let’s start by looking at the site distribution. We can color the sites based on their designated category (urban vs rural). We will also highlight Wilderness Park due to collections that happen there for the activity monitoring of Agelenopsis pennsylvanica. We will also fill the site labels of the sites where A. pennsylvanica are collected for the lab choice test.

Sites are randomly distributed across Lincoln, Nebraska (urban) and into the surrounding rural area (rural).

Let’s now take a look at a summary of the vibratory noise recorded across the experiment. Here, we order sites by the lowest average Leq (left) to the highest average Leq (right). Sites are divided along the horizontal axis and further divided by visit number. Microphones are divided across the vertical axis, further separated by substrate and hour. Each box represents the average Leq of an hour. Missing bars indicate failed recording trials (i.e., the microphone fell during recording or received damage from wildlife).

Spatial Variation in Vibratory Noise

Does vibratory noise vary over space? Let’s first look at average Leq across sites. We will use all 23 sites.

Site averages vary from -55 dB to -70 dB (~15 dB difference). The sites with the highest Leq appear to occur near highly traveled roads (i.e., highways/interstates).

Let’s test this hypothesis by using the PCA analysis that reduced measures of potential traffic impact and see if that predicts vibratory noise levels.

Let’s first take a look at the raw data:

Let’s explore distributions:

We performed lmer ( mean_leq ~ Dim.1 * Substrate + (1 | Site) , data = dayavgl ) overall and for category subsets and found:

Chisq Df Pr(>Chisq)
Category 18.2275 1 1.96e-05
Chisq Df Pr(>Chisq)
Dim.1 34.3476204 1 0.0000000
Substrate 34.8981142 1 0.0000000
Dim.1:Substrate 0.4780334 1 0.4893144
Chisq Df Pr(>Chisq)
Dim.1 19.8900245 1 0.0000082
Substrate 34.6037871 1 0.0000000
Dim.1:Substrate 0.3286133 1 0.5664768
Chisq Df Pr(>Chisq)
Dim.1 0.0201165 1 0.8872123
Substrate 3.4653578 1 0.0626669
Dim.1:Substrate 4.2373020 1 0.0395450

Let’s check the assumptions:

Let’s graph the results:

By Category

Urban sites had louder daily vibrations than rural sites (Chisq = 18.23, df = 2, 295, P < 0.001).

Overall

Daily average Leq has a significant positive relationship with Principal Component 1 - road vibratory noise (Chisq = 34.35, df = 4, 295, P < 0.001, cond R\(^2\) = 0.81, marg R\(^2\) = 0.51). Daily average Leq was significantly higher on manmade material than plant material (Chisq = 34.9, df = 4, 295, P < 0.001). There is no interaction (Chisq = 0.4780334, df = 4, 295, P = 0.489).

Urban Subset

Daily average Leq has a significant positive relationship with Principal Component 1 - road vibratory noise (Chisq = 19.89, df = 4, 217, P < 0.001, cond R\(^2\) = 0.76, marg R\(^2\) = 0.44). Daily average Leq was significantly higher on manmade material than plant material (Chisq = 34.6, df = 4, 217, P < 0.001). There is no interaction (Chisq = 0.3286133, df = 4, 217, P = 0.566).

Rural Subset

There was no sigificant correlation between daily average Leq and Principal Component 1 - road vibratory noise (Chisq = 0.02, df = 4, 78, P = 0.887, cond R\(^2\) = 0.44, marg R\(^2\) = 0.06). There was a trend that daily average Leq was higher on manmade material than plant material (Chisq = 3.47, df = 4, 78, P = 0.063). There is a significant interaction between daily average Leq and Principal Component 1 (Chisq = 4.237302, df = 4, 78, P = 0.04) - vibrations on manmade substrates was louder at the lowest road vibratory noise, but did not differ from plants at higher levels.

Let’s take a closer look at the substrate.

Manmade structures - Paneling, Metal, Concrete, Brick, Wood

Plant structures - Herb, Tree, Shrub, Vine

Brick, paneling, and shrubs carried the highest amplitude vibrations Bricks and herbs have the steepest slopes, which might suggest these substrates are affected by vibratory noise. Wood in quiet areas have high vibrations, probably as a result of people and pets walking on porches.

Temporal Variation in Vibratory Noise

Season

It seems like the rural sites got louder on the third visit.

We performed lmer ( mean_leq ~ Visit * Category + (1 | Site) , data = dayavgl_20 ) and found:

Chisq Df Pr(>Chisq)
Visit 7.362416 3 0.061201
Category 20.493744 1 0.000006
Visit:Category 3.492939 3 0.321679 
## $`emmeans of Visit`
##  Visit emmean    SE   df lower.CL upper.CL
##  1      -65.5 0.650 27.1    -66.8    -64.2
##  2      -65.8 0.634 24.5    -67.1    -64.5
##  3      -64.6 0.643 25.9    -65.9    -63.3
##  4      -65.2 0.631 24.0    -66.5    -63.9
## 
## Results are averaged over the levels of: Category 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Visit`
##  1               estimate    SE  df t.ratio p.value
##  Visit1 - Visit2    0.279 0.392 242   0.711  0.8927
##  Visit1 - Visit3   -0.867 0.407 242  -2.129  0.1468
##  Visit1 - Visit4   -0.260 0.387 242  -0.671  0.9081
##  Visit2 - Visit3   -1.146 0.382 241  -3.002  0.0156
##  Visit2 - Visit4   -0.539 0.360 241  -1.498  0.4401
##  Visit3 - Visit4    0.607 0.376 241   1.614  0.3728
## 
## Results are averaged over the levels of: Category 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Let’s check the assumptions:

Let’s graph the results:

To investigate whether noise varied across the season, we used a linear mixed model with visit number and category, and their interaction with site as a random factor. There was a trend that daily average Leq varied across the 2020 season (Chisq = 7.36, df = 8, 268, P = 0.061, cond R\(^2\) = 0.75, marg R\(^2\) = 0.39). A post hoc test suggests that visit 3 was significantly louder than visit 2 (t = -3.002, P = 0.016). Also, urban areas are louder than rural areas (Chisq = 20.49, P < 0.001). There is no interaction between visit and category (Chisq = 3.49, P = 0.322).

Let’s look at date rather than visit.

We performed lmer ( mean_leq ~ Day * Category + (1 | Site) , data = dayavgl_20 ) and found:

Chisq Df Pr(>Chisq)
Day 1.556506 1 0.2121775
Category 21.234293 1 0.0000041
Day:Category 1.813069 1 0.1781405

Let’s graph the results:

We see similar results by category (Chisq = 21.23, df = 4, 268, P < 0.001), but with no difference over time (Chisq = 1.56, P = 0.212, cond R\(^2\) = 0.75, marg R\(^2\) = 0.39) and no interaction (Chisq = 1.81, P = 0.178).

It seems like rural environments might be changing more over time. Let’s investigate whether harvest might play a role.

We performed lmer ( mean_leq ~ mean_harvest + (1 | Site) , data = dayavgl_20_rural ) and found:

Chisq Df Pr(>Chisq)
mean_harvest 4.497504 1 0.0339444

Let’s check the assumptions:

Let’s graph the results:

We used USDA data on week end percent harvest in 2020 for field crops in Nebraska. This gave details on oats, wheat, dry beans, sorghum, corn, and soybeans. We restricted this list to corn and soybeans, as these are the major crops grown and harvested in Lancaster County, Nebraska. We took the mean week end percent harvested of these two crops during the study season and compared these to the rural recorded vibratory noise levels. The week end percent harvested was positively correlated with the daily average Leq for rural sites (Chisq = 4.5, df = 2, 78, P = 0.034, cond R\(^2\) = 0.38, marg R\(^2\) = 0.04).

24 Hours

Here we assessed how vibratory noise levels change throughout the day. We graph the calculated mean and standard error. The grey areas represent nighttime. We added vertical dashed lines where vibratory noise peaked throughout the day, coinciding with rush hours. This provides further evidence that road noise likely represents a large component of vibratory noise. We also see what would likely be significant differences by category following the findings across season.